Overview

Dataset statistics

Number of variables13
Number of observations21,613
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)< 0.1%
Total size in memory2.1 MiB
Average record size in memory104.0 B

Variable types

Numeric9
Categorical4

Alerts

Dataset has 6 (< 0.1%) duplicate rowsDuplicates
bathrooms is highly overall correlated with bedrooms and 3 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
floors is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
grade is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
price is highly overall correlated with grade and 1 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
yr_renovated is highly imbalanced (74.7%)Imbalance

Reproduction

Analysis started2026-01-31 11:39:40.477637
Analysis finished2026-01-31 11:39:58.313364
Duration17.84 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

price
Real number (ℝ)

High correlation 

Distinct4029
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540088.58
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2026-01-31T17:09:58.470597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321950
median450000
Q3645000
95-th percentile1156480
Maximum7700000
Range7625000
Interquartile range (IQR)323050

Descriptive statistics

Standard deviation367126.83
Coefficient of variation (CV)0.67975299
Kurtosis34.585671
Mean540088.58
Median Absolute Deviation (MAD)150000
Skewness4.0240804
Sum1.1672934 × 1010
Variance1.3478211 × 1011
MonotonicityNot monotonic
2026-01-31T17:09:58.830851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000172
 
0.8%
450000172
 
0.8%
550000159
 
0.7%
500000152
 
0.7%
425000150
 
0.7%
325000148
 
0.7%
400000145
 
0.7%
375000138
 
0.6%
300000133
 
0.6%
525000131
 
0.6%
Other values (4019)20113
93.1%
ValueCountFrequency (%)
750001
< 0.1%
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
820001
< 0.1%
825001
< 0.1%
830001
< 0.1%
840001
< 0.1%
850002
< 0.1%
865001
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70625001
< 0.1%
68850001
< 0.1%
55700001
< 0.1%
53500001
< 0.1%
53000001
< 0.1%
51108001
< 0.1%
46680001
< 0.1%
45000001
< 0.1%
44890001
< 0.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3707954
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2026-01-31T17:09:59.050235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93010515
Coefficient of variation (CV)0.27593047
Kurtosis49.054564
Mean3.3707954
Median Absolute Deviation (MAD)1
Skewness1.9740277
Sum72853
Variance0.8650956
MonotonicityNot monotonic
2026-01-31T17:09:59.327875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
39823
45.4%
46882
31.8%
22761
 
12.8%
51601
 
7.4%
6272
 
1.3%
1199
 
0.9%
738
 
0.2%
013
 
0.1%
813
 
0.1%
96
 
< 0.1%
Other values (3)5
 
< 0.1%
ValueCountFrequency (%)
013
 
0.1%
1199
 
0.9%
22761
 
12.8%
39823
45.4%
46882
31.8%
51601
 
7.4%
6272
 
1.3%
738
 
0.2%
813
 
0.1%
96
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
111
 
< 0.1%
103
 
< 0.1%
96
 
< 0.1%
813
 
0.1%
738
 
0.2%
6272
 
1.3%
51601
 
7.4%
46882
31.8%
39823
45.4%

bathrooms
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1147573
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2026-01-31T17:09:59.652117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.77016316
Coefficient of variation (CV)0.36418512
Kurtosis1.2799024
Mean2.1147573
Median Absolute Deviation (MAD)0.5
Skewness0.51110757
Sum45706.25
Variance0.59315129
MonotonicityNot monotonic
2026-01-31T17:09:59.842647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.55380
24.9%
13852
17.8%
1.753048
14.1%
2.252047
 
9.5%
21930
 
8.9%
1.51446
 
6.7%
2.751185
 
5.5%
3753
 
3.5%
3.5731
 
3.4%
3.25589
 
2.7%
Other values (20)652
 
3.0%
ValueCountFrequency (%)
010
 
< 0.1%
0.54
 
< 0.1%
0.7572
 
0.3%
13852
17.8%
1.259
 
< 0.1%
1.51446
 
6.7%
1.753048
14.1%
21930
 
8.9%
2.252047
 
9.5%
2.55380
24.9%
ValueCountFrequency (%)
82
 
< 0.1%
7.751
 
< 0.1%
7.51
 
< 0.1%
6.752
 
< 0.1%
6.52
 
< 0.1%
6.252
 
< 0.1%
66
< 0.1%
5.754
 
< 0.1%
5.510
< 0.1%
5.2513
0.1%

sqft_living
Real number (ℝ)

High correlation 

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.8997
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2026-01-31T17:10:00.156682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11427
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1123

Descriptive statistics

Standard deviation918.4409
Coefficient of variation (CV)0.44157941
Kurtosis5.243093
Mean2079.8997
Median Absolute Deviation (MAD)540
Skewness1.4715554
Sum44952873
Variance843533.68
MonotonicityNot monotonic
2026-01-31T17:10:00.547002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300138
 
0.6%
1400135
 
0.6%
1440133
 
0.6%
1800129
 
0.6%
1660129
 
0.6%
1010129
 
0.6%
1820128
 
0.6%
1480125
 
0.6%
1720125
 
0.6%
1540124
 
0.6%
Other values (1028)20318
94.0%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
4101
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%
92001
< 0.1%
86701
< 0.1%
80201
< 0.1%
80101
< 0.1%
80001
< 0.1%

sqft_lot
Real number (ℝ)

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15106.968
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2026-01-31T17:10:00.848337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688
95-th percentile43339.2
Maximum1651359
Range1650839
Interquartile range (IQR)5648

Descriptive statistics

Standard deviation41420.512
Coefficient of variation (CV)2.7418151
Kurtosis285.07782
Mean15106.968
Median Absolute Deviation (MAD)2618
Skewness13.060019
Sum3.2650689 × 108
Variance1.7156588 × 109
MonotonicityNot monotonic
2026-01-31T17:10:01.134257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000358
 
1.7%
6000290
 
1.3%
4000251
 
1.2%
7200220
 
1.0%
4800120
 
0.6%
7500119
 
0.6%
4500114
 
0.5%
8400111
 
0.5%
9600109
 
0.5%
3600103
 
0.5%
Other values (9772)19818
91.7%
ValueCountFrequency (%)
5201
< 0.1%
5721
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
6381
< 0.1%
6492
< 0.1%
6511
< 0.1%
6751
< 0.1%
6761
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%
9822781
< 0.1%
9204231
< 0.1%
8816541
< 0.1%
8712002
< 0.1%
8433091
< 0.1%

floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494309
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2026-01-31T17:10:01.376706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5399889
Coefficient of variation (CV)0.36136361
Kurtosis-0.48472294
Mean1.494309
Median Absolute Deviation (MAD)0.5
Skewness0.61617672
Sum32296.5
Variance0.29158801
MonotonicityNot monotonic
2026-01-31T17:10:01.576738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
110680
49.4%
28241
38.1%
1.51910
 
8.8%
3613
 
2.8%
2.5161
 
0.7%
3.58
 
< 0.1%
ValueCountFrequency (%)
110680
49.4%
1.51910
 
8.8%
28241
38.1%
2.5161
 
0.7%
3613
 
2.8%
3.58
 
< 0.1%
ValueCountFrequency (%)
3.58
 
< 0.1%
3613
 
2.8%
2.5161
 
0.7%
28241
38.1%
1.51910
 
8.8%
110680
49.4%

waterfront
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
21450 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21,613
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Length

2026-01-31T17:10:01.821623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-31T17:10:01.990756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

view
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
19489 
2
 
963
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21,613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Length

2026-01-31T17:10:02.180887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-31T17:10:02.358457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
3
14031 
4
5679 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21,613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Length

2026-01-31T17:10:02.611634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-31T17:10:02.871136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring characters

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

grade
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6568732
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2026-01-31T17:10:03.048050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1754588
Coefficient of variation (CV)0.15351681
Kurtosis1.1909321
Mean7.6568732
Median Absolute Deviation (MAD)1
Skewness0.7711032
Sum165488
Variance1.3817033
MonotonicityNot monotonic
2026-01-31T17:10:03.259771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
78981
41.6%
86068
28.1%
92615
 
12.1%
62038
 
9.4%
101134
 
5.2%
11399
 
1.8%
5242
 
1.1%
1290
 
0.4%
429
 
0.1%
1313
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
33
 
< 0.1%
429
 
0.1%
5242
 
1.1%
62038
 
9.4%
78981
41.6%
86068
28.1%
92615
 
12.1%
101134
 
5.2%
11399
 
1.8%
ValueCountFrequency (%)
1313
 
0.1%
1290
 
0.4%
11399
 
1.8%
101134
 
5.2%
92615
 
12.1%
86068
28.1%
78981
41.6%
62038
 
9.4%
5242
 
1.1%
429
 
0.1%

yr_renovated
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
20699 
1
 
914

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21,613
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
020699
95.8%
1914
 
4.2%

Length

2026-01-31T17:10:03.436158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-31T17:10:03.555823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
020699
95.8%
1914
 
4.2%

Most occurring characters

ValueCountFrequency (%)
020699
95.8%
1914
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
020699
95.8%
1914
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
020699
95.8%
1914
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
020699
95.8%
1914
 
4.2%

lat
Real number (ℝ)

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560053
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2026-01-31T17:10:03.706136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.471
median47.5718
Q347.678
95-th percentile47.74964
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207

Descriptive statistics

Standard deviation0.13856371
Coefficient of variation (CV)0.0029134474
Kurtosis-0.676313
Mean47.560053
Median Absolute Deviation (MAD)0.1049
Skewness-0.48527048
Sum1027915.4
Variance0.019199902
MonotonicityNot monotonic
2026-01-31T17:10:03.966541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.662417
 
0.1%
47.532217
 
0.1%
47.684617
 
0.1%
47.549117
 
0.1%
47.695516
 
0.1%
47.688616
 
0.1%
47.671116
 
0.1%
47.540215
 
0.1%
47.684215
 
0.1%
47.690415
 
0.1%
Other values (5024)21452
99.3%
ValueCountFrequency (%)
47.15591
< 0.1%
47.15931
< 0.1%
47.16221
< 0.1%
47.16471
< 0.1%
47.17641
< 0.1%
47.17751
< 0.1%
47.17762
< 0.1%
47.17951
< 0.1%
47.18031
< 0.1%
47.18081
< 0.1%
ValueCountFrequency (%)
47.77763
< 0.1%
47.77753
< 0.1%
47.77741
 
< 0.1%
47.77723
< 0.1%
47.77712
 
< 0.1%
47.7772
 
< 0.1%
47.77693
< 0.1%
47.77682
 
< 0.1%
47.77676
< 0.1%
47.77664
< 0.1%

long
Real number (ℝ)

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2139
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21613
Negative (%)100.0%
Memory size169.0 KiB
2026-01-31T17:10:04.209218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14082834
Coefficient of variation (CV)-0.0011523104
Kurtosis1.0495009
Mean-122.2139
Median Absolute Deviation (MAD)0.101
Skewness0.88505298
Sum-2641408.9
Variance0.019832622
MonotonicityNot monotonic
2026-01-31T17:10:04.428819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29116
 
0.5%
-122.3111
 
0.5%
-122.362104
 
0.5%
-122.291100
 
0.5%
-122.36399
 
0.5%
-122.37299
 
0.5%
-122.28898
 
0.5%
-122.35796
 
0.4%
-122.28495
 
0.4%
-122.36594
 
0.4%
Other values (742)20601
95.3%
ValueCountFrequency (%)
-122.5191
 
< 0.1%
-122.5151
 
< 0.1%
-122.5141
 
< 0.1%
-122.5121
 
< 0.1%
-122.5112
< 0.1%
-122.5092
< 0.1%
-122.5071
 
< 0.1%
-122.5061
 
< 0.1%
-122.5053
< 0.1%
-122.5042
< 0.1%
ValueCountFrequency (%)
-121.3152
< 0.1%
-121.3161
< 0.1%
-121.3191
< 0.1%
-121.3211
< 0.1%
-121.3251
< 0.1%
-121.3522
< 0.1%
-121.3591
< 0.1%
-121.3642
< 0.1%
-121.4021
< 0.1%
-121.4031
< 0.1%

Interactions

2026-01-31T17:09:55.412060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:41.601473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:43.201896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:45.162255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:46.959163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:48.554976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:50.833654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:52.324937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:53.903770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:55.570933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:41.726481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:43.391744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:45.383404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:47.145023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:49.373817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:51.003357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:52.507922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:54.080978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:55.745768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:41.842256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:43.571729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:45.621651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:47.319611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:49.561583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:51.184077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:52.631269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:54.258644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:55.888137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:41.977995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:43.829508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:45.804827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:47.475205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:49.783416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:51.355476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:52.828082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:54.406003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:56.076930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:42.120423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:44.071507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:46.001385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:47.675790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:49.960973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:51.516624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:53.010185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:54.583436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:56.274517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:42.243335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:44.312251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:46.182761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:47.865584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:50.144428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:51.674222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:53.183139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:54.744694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:56.931390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:42.357738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:44.471544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:46.325888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:48.067052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:50.292654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:51.820270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:53.418185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:54.916020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:57.115180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:42.688174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:44.676199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:46.472336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:48.240278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:50.455158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:52.014726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:53.578266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:55.087398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:57.366740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:42.961675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:44.937840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:46.740892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:48.399183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:50.647275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:52.185125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:53.736676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-31T17:09:55.251405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-31T17:10:04.615069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradelatlongpricesqft_livingsqft_lotviewwaterfrontyr_renovated
bathrooms1.0000.5210.1300.5470.6580.0080.2620.4970.7460.0690.1140.1020.060
bedrooms0.5211.0000.0240.2280.381-0.0210.1910.3450.6470.2170.0380.0000.021
condition0.1300.0241.0000.1790.1540.0580.0810.0230.0600.0390.0250.0170.067
floors0.5470.2280.1791.0000.5020.0250.1490.3220.401-0.2340.0240.0220.059
grade0.6580.3810.1540.5021.0000.1040.2230.6580.7160.1520.1430.1180.013
lat0.008-0.0210.0580.0250.1041.000-0.1430.4560.031-0.1220.0680.0340.059
long0.2620.1910.0810.1490.223-0.1431.0000.0640.2850.3710.0850.0960.083
price0.4970.3450.0230.3220.6580.4560.0641.0000.6440.0750.2080.3200.128
sqft_living0.7460.6470.0600.4010.7160.0310.2850.6441.0000.3040.1490.1400.071
sqft_lot0.0690.2170.039-0.2340.152-0.1220.3710.0750.3041.0000.0400.0140.000
view0.1140.0380.0250.0240.1430.0680.0850.2080.1490.0401.0000.5920.109
waterfront0.1020.0000.0170.0220.1180.0340.0960.3200.1400.0140.5921.0000.092
yr_renovated0.0600.0210.0670.0590.0130.0590.0830.1280.0710.0000.1090.0921.000

Missing values

2026-01-31T17:09:57.723901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-31T17:09:58.071115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradeyr_renovatedlatlong
0231300.021.00118056501.00037047.5112-122.257
1538000.032.25257072422.00037147.7210-122.319
2180000.021.00770100001.00036047.7379-122.233
3604000.043.00196050001.00057047.5208-122.393
4510000.032.00168080801.00038047.6168-122.045
51225000.044.5054201019301.000311047.6561-122.005
6257500.032.25171568192.00037047.3097-122.327
7291850.031.50106097111.00037047.4095-122.315
8229500.031.00178074701.00037047.5123-122.337
9323000.032.50189065602.00037047.3684-122.031
pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradeyr_renovatedlatlong
21603507250.032.50227055362.00038047.5389-121.881
21604429000.032.00149011263.00038047.5699-122.288
21605610685.042.50252060232.00039047.5137-122.167
216061007500.043.50351072002.00039047.5537-122.398
21607475000.032.50131012942.00038047.5773-122.409
21608360000.032.50153011313.00038047.6993-122.346
21609400000.042.50231058132.00038047.5107-122.362
21610402101.020.75102013502.00037047.5944-122.299
21611400000.032.50160023882.00038047.5345-122.069
21612325000.020.75102010762.00037047.5941-122.299

Duplicate rows

Most frequently occurring

pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradeyr_renovatedlatlong# duplicates
0259950.022.0010706492.00039047.5213-122.3572
1529500.032.2514109053.00039047.5818-122.4022
2550000.041.75241084472.00348147.6499-122.0882
3555000.032.50194032112.00038047.5644-122.0932
4585000.032.50229050892.00039047.5443-122.1722
5635000.033.00223014072.50038047.5446-122.0172